

'''simple docstring'''





import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase_    						=       """▁"""

UpperCamelCase_    						=       get_tests_dir("""fixtures/test_sentencepiece.model""")





@require_sentencepiece
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    BertGenerationTokenizer
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    True







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										super().setUp()

										lowercase :							List[str]    		=BertGenerationTokenizer(UpperCAmelCase__		,					keep_accents=UpperCAmelCase__   )
										tokenizer.save_pretrained(self.tmpdirname   )







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							int    		='''<s>'''
										lowercase :							List[Any]    		=1

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__   )		,					UpperCAmelCase__   )
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__   )		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=list(self.get_tokenizer().get_vocab().keys()   )

										self.assertEqual(vocab_keys[0]		,					'''<unk>'''   )
										self.assertEqual(vocab_keys[1]		,					'''<s>'''   )
										self.assertEqual(vocab_keys[-1]		,					'''<pad>'''   )
										self.assertEqual(len(UpperCAmelCase__   )		,					1002   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										self.assertEqual(self.get_tokenizer().vocab_size		,					1000   )







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							Any    		=BertGenerationTokenizer(UpperCAmelCase__		,					keep_accents=UpperCAmelCase__   )

										lowercase :							Optional[Any]    		=tokenizer.tokenize('''This is a test'''   )
										self.assertListEqual(UpperCAmelCase__		,					['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(UpperCAmelCase__   )		,					[285, 46, 10, 170, 382]		,					)

										lowercase :							Tuple    		=tokenizer.tokenize('''I was born in 92000, and this is falsé.'''   )
										self.assertListEqual(
										    UpperCAmelCase__		,					[
										        SPIECE_UNDERLINE + '''I''',
										        SPIECE_UNDERLINE + '''was''',
										        SPIECE_UNDERLINE + '''b''',
										        '''or''',
										        '''n''',
										        SPIECE_UNDERLINE + '''in''',
										        SPIECE_UNDERLINE + '''''',
										        '''9''',
										        '''2''',
										        '''0''',
										        '''0''',
										        '''0''',
										        ''',''',
										        SPIECE_UNDERLINE + '''and''',
										        SPIECE_UNDERLINE + '''this''',
										        SPIECE_UNDERLINE + '''is''',
										        SPIECE_UNDERLINE + '''f''',
										        '''al''',
										        '''s''',
										        '''é''',
										        '''.''',
										    ]		,					)
										lowercase :							List[Any]    		=tokenizer.convert_tokens_to_ids(UpperCAmelCase__   )
										self.assertListEqual(
										    UpperCAmelCase__		,					[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]		,					)

										lowercase :							Any    		=tokenizer.convert_ids_to_tokens(UpperCAmelCase__   )
										self.assertListEqual(
										    UpperCAmelCase__		,					[
										        SPIECE_UNDERLINE + '''I''',
										        SPIECE_UNDERLINE + '''was''',
										        SPIECE_UNDERLINE + '''b''',
										        '''or''',
										        '''n''',
										        SPIECE_UNDERLINE + '''in''',
										        SPIECE_UNDERLINE + '''''',
										        '''<unk>''',
										        '''2''',
										        '''0''',
										        '''0''',
										        '''0''',
										        ''',''',
										        SPIECE_UNDERLINE + '''and''',
										        SPIECE_UNDERLINE + '''this''',
										        SPIECE_UNDERLINE + '''is''',
										        SPIECE_UNDERLINE + '''f''',
										        '''al''',
										        '''s''',
										        '''<unk>''',
										        '''.''',
										    ]		,					)







				@cached_property
				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''   )







				@slow
				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		='''Hello World!'''
										lowercase :							Dict    		=[18536, 2260, 101]

										self.assertListEqual(UpperCAmelCase__		,					self.big_tokenizer.encode(UpperCAmelCase__   )   )







				@slow
				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							Tuple    		=(
										    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
										    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
										)
										lowercase :							Optional[int]    		=[
										    871,
										    419,
										    358,
										    946,
										    991,
										    2521,
										    452,
										    358,
										    1357,
										    387,
										    7751,
										    3536,
										    112,
										    985,
										    456,
										    126,
										    865,
										    938,
										    5400,
										    5734,
										    458,
										    1368,
										    467,
										    786,
										    2462,
										    5246,
										    1159,
										    633,
										    865,
										    4519,
										    457,
										    582,
										    852,
										    2557,
										    427,
										    916,
										    508,
										    405,
										    34324,
										    497,
										    391,
										    408,
										    11342,
										    1244,
										    385,
										    100,
										    938,
										    985,
										    456,
										    574,
										    362,
										    12597,
										    3200,
										    3129,
										    1172,
										]

										self.assertListEqual(UpperCAmelCase__		,					self.big_tokenizer.encode(UpperCAmelCase__   )   )







				@require_torch
				@slow
				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										import torch

										from transformers import BertGenerationConfig, BertGenerationEncoder

										# Build sequence
										lowercase :							Union[str, Any]    		=list(self.big_tokenizer.get_vocab().keys()   )[:10]
										lowercase :							Union[str, Any]    		=''' '''.join(UpperCAmelCase__   )
										lowercase :							Optional[int]    		=self.big_tokenizer.encode_plus(UpperCAmelCase__		,					return_tensors='''pt'''		,					return_token_type_ids=UpperCAmelCase__   )
										lowercase :							Optional[Any]    		=self.big_tokenizer.batch_encode_plus(
										    [sequence + ''' ''' + sequence]		,					return_tensors='''pt'''		,					return_token_type_ids=UpperCAmelCase__   )

										lowercase :							str    		=BertGenerationConfig()
										lowercase :							str    		=BertGenerationEncoder(UpperCAmelCase__   )

										assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

										with torch.no_grad():
																model(**UpperCAmelCase__   )
																model(**UpperCAmelCase__   )







				@slow
				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										# fmt: off
										lowercase :							Optional[Any]    		={'''input_ids''': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=UpperCAmelCase__		,					model_name='''google/bert_for_seq_generation_L-24_bbc_encoder'''		,					revision='''c817d1fd1be2ffa69431227a1fe320544943d4db'''		,					)






import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     __lowerCAmelCase		):







   @register_to_config
   def __init__(     self						:						List[Any]   ,   lowerCamelCase_						:						int = 128   ,   lowerCamelCase_						:						int = 256   ,   lowerCamelCase_						:						float = 2_0_0_0.0   ,   lowerCamelCase_						:						int = 768   ,   lowerCamelCase_						:						int = 12   ,   lowerCamelCase_						:						int = 12   ,   lowerCamelCase_						:						int = 64   ,   lowerCamelCase_						:						int = 2048   ,   lowerCamelCase_						:						float = 0.1   ,   ):





    """simple docstring"""


    super().__init__()

    UpperCamelCase			  =      nn.Sequential(
        nn.Linear(lowerCamelCase_   ,   d_model * 4   ,   bias=lowerCamelCase_				)   ,   nn.SiLU()   ,   nn.Linear(d_model * 4   ,   d_model * 4   ,   bias=lowerCamelCase_				)   ,   nn.SiLU()   ,   )

    UpperCamelCase			  =      nn.Embedding(lowerCamelCase_   ,   lowerCamelCase_				)
    UpperCamelCase			  =      False

    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)

    UpperCamelCase			  =      nn.Dropout(p=lowerCamelCase_				)

    UpperCamelCase			  =      nn.ModuleList()
    for lyr_num in range(lowerCamelCase_				):
     # FiLM conditional T5 decoder
     UpperCamelCase			  =      DecoderLayer(d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)
     self.decoders.append(lowerCamelCase_				)

    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_				)

    UpperCamelCase			  =      nn.Dropout(p=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      torch.mul(query_input.unsqueeze(-1				)   ,   key_input.unsqueeze(-2				)				)
    return mask.unsqueeze(-3				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      decoder_input_tokens.shape
    assert decoder_noise_time.shape == (batch,)

    # decoder_noise_time is in [0, 1), so rescale to expected timing range.
    UpperCamelCase			  =      get_timestep_embedding(
        decoder_noise_time * self.config.max_decoder_noise_time   ,   embedding_dim=self.config.d_model   ,   max_period=self.config.max_decoder_noise_time   ,   ).to(dtype=self.dtype				)

    UpperCamelCase			  =      self.conditioning_emb(lowerCamelCase_				).unsqueeze(1				)

    assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

    UpperCamelCase			  =      decoder_input_tokens.shape[1]

    # If we want to use relative positions for audio context, we can just offset
    # this sequence by the length of encodings_and_masks.
    UpperCamelCase			  =      torch.broadcast_to(
        torch.arange(lowerCamelCase_   ,   device=decoder_input_tokens.device				)   ,   (batch, seq_length)   ,   )

    UpperCamelCase			  =      self.position_encoding(lowerCamelCase_				)

    UpperCamelCase			  =      self.continuous_inputs_projection(lowerCamelCase_				)
    inputs += position_encodings
    UpperCamelCase			  =      self.dropout(lowerCamelCase_				)

    # decoder: No padding present.
    UpperCamelCase			  =      torch.ones(
        decoder_input_tokens.shape[:2]   ,   device=decoder_input_tokens.device   ,   dtype=inputs.dtype				)

    # Translate encoding masks to encoder-decoder masks.
    UpperCamelCase			  =      [(x, self.encoder_decoder_mask(lowerCamelCase_   ,   lowerCamelCase_				)) for x, y in encodings_and_masks]

    # cross attend style: concat encodings
    UpperCamelCase			  =      torch.cat([x[0] for x in encodings_and_encdec_masks]   ,   dim=1				)
    UpperCamelCase			  =      torch.cat([x[1] for x in encodings_and_encdec_masks]   ,   dim=-1				)

    for lyr in self.decoders:
     UpperCamelCase			  =      lyr(
         lowerCamelCase_   ,   conditioning_emb=lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   encoder_attention_mask=lowerCamelCase_   ,   )[0]

    UpperCamelCase			  =      self.decoder_norm(lowerCamelCase_				)
    UpperCamelCase			  =      self.post_dropout(lowerCamelCase_				)

    UpperCamelCase			  =      self.spec_out(lowerCamelCase_				)
    return spec_out





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						List[str]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Any=1E-6				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.ModuleList()

    # cond self attention: layer 0
    self.layer.append(
        TaLayerSelfAttentionCond(d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)				)

    # cross attention: layer 1
    self.layer.append(
        TaLayerCrossAttention(
            d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_   ,   layer_norm_epsilon=lowerCamelCase_   ,   )				)

    # Film Cond MLP + dropout: last layer
    self.layer.append(
        TaLayerFFCond(d_model=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_   ,   layer_norm_epsilon=lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						List[Any]=None   ,   lowerCamelCase_						:						int=None   ,   lowerCamelCase_						:						Optional[int]=None   ,   lowerCamelCase_						:						Any=None   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer[0](
        lowerCamelCase_   ,   conditioning_emb=lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   )

    if encoder_hidden_states is not None:
     UpperCamelCase			  =      torch.where(encoder_attention_mask > 0   ,   0   ,   -1E10				).to(
         encoder_hidden_states.dtype				)

     UpperCamelCase			  =      self.layer[1](
         lowerCamelCase_   ,   key_value_states=lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   )

    # Apply Film Conditional Feed Forward layer
    UpperCamelCase			  =      self.layer[-1](lowerCamelCase_   ,   lowerCamelCase_				)

    return (hidden_states,)





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_				)
    UpperCamelCase			  =      TaFiLMLayer(in_features=d_model * 4   ,   out_features=lowerCamelCase_				)
    UpperCamelCase			  =      Attention(query_dim=lowerCamelCase_   ,   heads=lowerCamelCase_   ,   dim_head=lowerCamelCase_   ,   out_bias=lowerCamelCase_   ,   scale_qk=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Tuple=None   ,   lowerCamelCase_						:						Optional[Any]=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)

    if conditioning_emb is not None:
     UpperCamelCase			  =      self.FiLMLayer(lowerCamelCase_   ,   lowerCamelCase_				)

    # Self-attention block
    UpperCamelCase			  =      self.attention(lowerCamelCase_				)

    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)

    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      Attention(query_dim=lowerCamelCase_   ,   heads=lowerCamelCase_   ,   dim_head=lowerCamelCase_   ,   out_bias=lowerCamelCase_   ,   scale_qk=lowerCamelCase_				)
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_   ,   eps=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Dict=None   ,   lowerCamelCase_						:						int=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)
    UpperCamelCase			  =      self.attention(
        lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   attention_mask=attention_mask.squeeze(1				)   ,   )
    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)
    return layer_output





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      TaDenseGatedActDense(d_model=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)
    UpperCamelCase			  =      TaFiLMLayer(in_features=d_model * 4   ,   out_features=lowerCamelCase_				)
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_   ,   eps=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						int=None				):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)
    if conditioning_emb is not None:
     UpperCamelCase			  =      self.film(lowerCamelCase_   ,   lowerCamelCase_				)

    UpperCamelCase			  =      self.DenseReluDense(lowerCamelCase_				)
    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)
    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						List[Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)
    UpperCamelCase			  =      NewGELUActivation()







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      self.act(self.wi_a(lowerCamelCase_				)				)
    UpperCamelCase			  =      self.wi_a(lowerCamelCase_				)
    UpperCamelCase			  =      hidden_gelu * hidden_linear
    UpperCamelCase			  =      self.dropout(lowerCamelCase_				)

    UpperCamelCase			  =      self.wo(lowerCamelCase_				)
    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Optional[int]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Dict=1E-6				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Parameter(torch.ones(lowerCamelCase_				)				)
    UpperCamelCase			  =      eps







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      hidden_states.to(torch.floataa				).pow(2				).mean(-1   ,   keepdim=lowerCamelCase_				)
    UpperCamelCase			  =      hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

    # convert into half-precision if necessary
    if self.weight.dtype in [torch.floataa, torch.bfloataa]:
     UpperCamelCase			  =      hidden_states.to(self.weight.dtype				)

    return self.weight * hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						torch.Tensor				):





    """simple docstring"""


    return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.0_4_4_7_1_5 * torch.pow(lowerCamelCase_   ,   3.0				))				))





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   out_features * 2   ,   bias=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.scale_bias(lowerCamelCase_				)
    UpperCamelCase  ,     UpperCamelCase			  =      torch.chunk(lowerCamelCase_   ,   2   ,   -1				)
    UpperCamelCase			  =      x * (1 + scale) + shift
    return x
